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  <title>MusicRNN</title>
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<body>
  <h1>MusicRNN</h1>
  <p>
    <code>MusicRNN</code> implements Magenta's LSTM-based language models, which
    are useful if you want to generate a <code>NoteSequence</code> to continue an
    input one. In the demos below, we call <code>continueSequence()</code> with the
    provided input, and display the result. Each section uses a different model
    checkpoint (melody, drums or chord pitches) to generate the output sequence.
  </p>

  <h2>Melody Continuation</h2>
  <section>
    <p><b>Input NoteSequence: </b><code id="melody-cont-inputs">Loading...</code></p>
    <p><b>Result: </b><code id="melody-cont-results">Loading...</code></p>
    <p><b>It took: </b><code id="melody-cont-time"></code></p>

  </section>

  <h2>Drums Continuation</h2>
  <section>
    <p><b>Input NoteSequence: </b><code id="drums-cont-inputs"></code></p>
    <p><b>Result: </b><code id="drums-cont-results">Loading...</code></p>
    <p><b>It took: </b><code id="drums-cont-time"></code></p>
  </section>

  <h2>Chord Pitches Improv Continuation</h2>
  <section>
    <p><b>Input NoteSequence: </b><code id="improv-cont-inputs"></code></p>
    <p><b>Result: </b><code id="improv-cont-results">Loading...</code></p>
    <p><b>It took: </b><code id="improv-cont-time"></code></p>
  </section>

  <section>
    <p><b>Total Leaked Memory:</b> <code id="leaked-memory"></code></b></p>
  </section>

  <script src="music_rnn_bundle.js"></script>
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